Dimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributions

A dimension reduction method for model-based clustering via a finite mixture of shifted asymmetric Laplace distributions is introduced. The approach is based on existing work within the Gaussian paradigm and relies on identification of a reduced subspace. This subspace contains linear combinations of the original data, ordered by importance using the associated eigenvalues. This clustering approach is illustrated on simulated and real data, …